Yu Huanzhou, Shimakawa Ann, McKenzie Charles A, Brodsky Ethan, Brittain Jean H, Reeder Scott B
Global MR Applied Science Lab, GE Healthcare, Menlo Park, California, USA.
Magn Reson Med. 2008 Nov;60(5):1122-34. doi: 10.1002/mrm.21737.
Multiecho chemical shift-based water-fat separation methods are seeing increasing clinical use due to their ability to estimate and correct for field inhomogeneities. Previous chemical shift-based water-fat separation methods used a relatively simple signal model that assumes both water and fat have a single resonant frequency. However, it is well known that fat has several spectral peaks. This inaccuracy in the signal model results in two undesired effects. First, water and fat are incompletely separated. Second, methods designed to estimate T(2) () in the presence of fat incorrectly estimate the T(2) () decay in tissues containing fat. In this work, a more accurate multifrequency model of fat is included in the iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) water-fat separation and simultaneous T(2) () estimation techniques. The fat spectrum can be assumed to be constant in all subjects and measured a priori using MR spectroscopy. Alternatively, the fat spectrum can be estimated directly from the data using novel spectrum self-calibration algorithms. The improvement in water-fat separation and T(2) () estimation is demonstrated in a variety of in vivo applications, including knee, ankle, spine, breast, and abdominal scans.
基于多回波化学位移的水脂分离方法因其能够估计和校正场不均匀性而在临床上得到越来越广泛的应用。以往基于化学位移的水脂分离方法使用的是相对简单的信号模型,该模型假定水和脂肪都只有一个共振频率。然而,众所周知,脂肪有多个光谱峰。信号模型中的这种不准确性会导致两种不良影响。第一,水和脂肪不能完全分离。第二,在有脂肪存在的情况下用于估计T(2)*的方法会错误地估计含脂肪组织中的T(2)*衰减。在这项工作中,在具有回波不对称性和最小二乘估计(IDEAL)的水脂分离及同时T(2)*估计技术的水脂迭代分解中纳入了更准确的脂肪多频模型。脂肪光谱可以假定在所有受试者中是恒定的,并使用磁共振波谱事先进行测量。或者,脂肪光谱可以使用新颖的光谱自校准算法直接从数据中估计出来。在包括膝盖、脚踝、脊柱、乳房和腹部扫描在内的各种体内应用中都证明了水脂分离和T(2)*估计方面的改进。